ML Ops Infrastructure Engineer

Deepgram is the leading platform underpinning the emerging trillion-dollar Voice AI economy, providing real-time APIs for speech-to-text (STT), text-to-speech (TTS), and building production-grade voice agents at scale. More than 200,000 developers and 1,300+ organizations build voice offerings that are ‘Powered by Deepgram’, including Twilio, Cloudflare, Sierra, Decagon, Vapi, Daily, Cresta, Granola, and Jack in the Box. Deepgram’s voice-native foundation models are accessed through cloud APIs or as self-hosted and on-premises software, with unmatched accuracy, low latency, and cost efficiency. Backed by a recent Series C led by leading global investors and strategic partners, Deepgram has processed over 50,000 years of audio and transcribed more than 1 trillion words. There is no organization in the world that understands voice better than Deepgram.

At Deepgram, we expect an AI-first mindset—AI use and comfort aren’t optional, they’re core to how we operate, innovate, and measure performance.

Every team member who works at Deepgram is expected to actively use and experiment with advanced AI tools, and even build your own into your everyday work. We measure how effectively AI is applied to deliver results, and consistent, creative use of the latest AI capabilities is key to success here. Candidates should be comfortable adopting new models and modes quickly, integrating AI into their workflows, and continuously pushing the boundaries of what these technologies can do.

Additionally, we move at the pace of AI. Change is rapid, and you can expect your day-to-day work to evolve just as quickly. This may not be the right role if you’re not excited to experiment, adapt, think on your feet, and learn constantly, or if you’re seeking something highly prescriptive with a traditional 9-to-5.

Getting a model from a research notebook to a production API serving millions of requests is one of the hardest problems in AI. As an ML Ops Infrastructure Engineer at Deepgram, you will own the critical bridge between research and production -- building the pipelines, deployment systems, and testing infrastructure that take models from experimental to battle-tested at scale. Your work ensures that every model improvement our research team makes can be safely, quickly, and reliably delivered to the customers who depend on Deepgram's APIs for real-time voice AI.

• Design and build CI/CD pipelines specifically tailored for ML model development, validation, and deployment

• Architect and maintain model deployment pipelines that move models from research environments through staging to production with confidence

• Build A/B testing infrastructure that enables controlled rollouts of new models and measures real-world performance impact

• Implement comprehensive monitoring for model performance in production -- accuracy metrics, latency, drift detection, and regression alerts

• Develop automated retraining pipelines that trigger on data changes, performance degradation, or scheduled cadences

• Create and maintain build and test environments that mirror production, giving researchers high-fidelity feedback before deployment

• Establish model versioning, artifact management, and rollback capabilities to ensure safe and reproducible deployments

• Collaborate with research engineers to define and enforce model quality gates before production promotion

• Build observability dashboards that give the team real-time insight into model health across all environments

• Optimize model serving infrastructure for latency, throughput, and cost efficiency
  

• Are excited by the challenge of operationalizing cutting-edge AI models at production scale

• Believe that great infrastructure is what turns research breakthroughs into customer value

• Enjoy designing systems that are automated, reliable, and self-healing

• Want to work on problems where minutes of latency reduction or percentage points of accuracy matter enormously

• Like collaborating across research and engineering teams to make the whole organization faster

• Are motivated by building the deployment and testing systems that back a platform serving over 200,000 developers
  

• 4+ years of experience in MLOps, DevOps, or infrastructure engineering with a focus on ML systems

• Strong proficiency in Python and experience building automation and tooling for ML workflows

• Deep experience with CI/CD systems and building pipelines for software and model delivery

• Hands-on experience with Docker and Kubernetes for containerized workload management

• Practical experience deploying and serving ML models in production environments

• Familiarity with model evaluation, validation, and quality assurance processes

• Understanding of monitoring and observability principles as applied to ML systems

• Strong problem-solving skills and a bias toward automation over manual processes
  

• Experience with model serving frameworks such as NVIDIA Triton Inference Server, TensorRT, or ONNX Runtime

• Background in speech, audio, or real-time media ML systems

• Experience with Infrastructure as Code tools such as Terraform or Pulumi

• Hands-on experience with monitoring and observability stacks (Prometheus, Grafana, Datadog, or similar)

• Familiarity with GPU-accelerated inference optimization and profiling

• Experience with feature stores, data versioning, or ML metadata management

• Knowledge of canary deployment strategies and progressive delivery for ML models

• Medical, dental, vision benefits

• Annual wellness stipend

• Mental health support

• Life, STD, LTD Income Insurance Plans

• Unlimited PTO

• Generous paid parental leave

• Flexible schedule

• 12 Paid US company holidays

• Quarterly personal productivity stipend

• One-time stipend for home office upgrades

• 401(k) plan with company match

• Tax Savings Programs

• Learning / Education stipend

• Participation in talks and conferences

• Employee Resource Groups

• AI enablement workshops / sessions

*For candidates outside of the US, we use an Employer of Record model in many countries, which means benefits are administered locally and governed by country-specific regulations. Because of this, benefits will differ by region — in some cases international employees receive benefits US employees do not, and vice versa. As we scale, we will continue to evaluate where we can create more alignment, but a 1:1 global benefits structure is not always legally or operationally possible.

Backed by prominent investors including Y Combinator, Madrona, Tiger Global, Wing VC and NVIDIA, Deepgram has raised over $215M in total funding. If you're looking to work on cutting-edge technology and make a significant impact in the AI industry, we'd love to hear from you!

Deepgram is an equal opportunity employer. We want all voices and perspectives represented in our workforce. We are a curious bunch focused on collaboration and doing the right thing. We put our customers first, grow together and move quickly. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, gender identity or expression, age, marital status, veteran status, disability status, pregnancy, parental status, genetic information, political affiliation, or any other status protected by the laws or regulations in the locations where we operate.

We are happy to provide accommodations for applicants who need them.